## Contents

function [z, history] = linprog(c, A, b, rho, alpha)
% linprog  Solve standard form LP via ADMM
%
% [x, history] = linprog(c, A, b, rho, alpha);
%
% Solves the following problem via ADMM:
%
%   minimize     c'*x
%   subject to   Ax = b, x >= 0
%
% The solution is returned in the vector x.
%
% history is a structure that contains the objective value, the primal and
% dual residual norms, and the tolerances for the primal and dual residual
% norms at each iteration.
%
% rho is the augmented Lagrangian parameter.
%
% alpha is the over-relaxation parameter (typical values for alpha are
% between 1.0 and 1.8).
%
%
%

t_start = tic;

QUIET    = 0;
MAX_ITER = 1000;
ABSTOL   = 1e-4;
RELTOL   = 1e-2;

## Data preprocessing

[m n] = size(A);

x = zeros(n,1);
z = zeros(n,1);
u = zeros(n,1);

if ~QUIET
fprintf('%3s\t%10s\t%10s\t%10s\t%10s\t%10s\n', 'iter', ...
'r norm', 'eps pri', 's norm', 'eps dual', 'objective');
end

for k = 1:MAX_ITER

% x-update
tmp = [ rho*eye(n), A'; A, zeros(m) ] \ [ rho*(z - u) - c; b ];
x = tmp(1:n);

% z-update with relaxation
zold = z;
x_hat = alpha*x + (1 - alpha)*zold;
z = pos(x_hat + u);

u = u + (x_hat - z);

% diagnostics, reporting, termination checks

history.objval(k)  = objective(c, x);

history.r_norm(k)  = norm(x - z);
history.s_norm(k)  = norm(-rho*(z - zold));

history.eps_pri(k) = sqrt(n)*ABSTOL + RELTOL*max(norm(x), norm(-z));
history.eps_dual(k)= sqrt(n)*ABSTOL + RELTOL*norm(rho*u);

if ~QUIET
fprintf('%3d\t%10.4f\t%10.4f\t%10.4f\t%10.4f\t%10.2f\n', k, ...
history.r_norm(k), history.eps_pri(k), ...
history.s_norm(k), history.eps_dual(k), history.objval(k));
end

if (history.r_norm(k) < history.eps_pri(k) && ...
history.s_norm(k) < history.eps_dual(k))
break;
end
end

if ~QUIET
toc(t_start);
end
end

function obj = objective(c, x)
obj = c'*x;
end